Incorporating Rivalry in Reinforcement Learning for a Competitive Game

• URL: http://arxiv.org/abs/2011.01337v1
• Date: Mon, 2 Nov 2020 21:54:18 GMT
• Title: Incorporating Rivalry in Reinforcement Learning for a Competitive Game
• Authors: Pablo Barros, Ana Tanevska, Ozge Yalcin, Alessandra Sciutti
• Abstract summary: This study focuses on providing a novel learning mechanism based on a rivalry social impact. Based on the concept of competitive rivalry, our analysis aims to investigate if we can change the assessment of these agents from a human perspective.
• Score: 65.2200847818153
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent advances in reinforcement learning with social agents have allowed us to achieve human-level performance on some interaction tasks. However, most interactive scenarios do not have as end-goal performance alone; instead, the social impact of these agents when interacting with humans is as important and, in most cases, never explored properly. This preregistration study focuses on providing a novel learning mechanism based on a rivalry social impact. Our scenario explored different reinforcement learning-based agents playing a competitive card game against human players. Based on the concept of competitive rivalry, our analysis aims to investigate if we can change the assessment of these agents from a human perspective.

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